System and method for determining the energy requirement of a vehicle for a journey

ABSTRACT

A system for determining an energy requirement of a vehicle for a journey. The system includes a predictor mechanism to predict, using an energy prediction algorithm, a vehicle energy requirement for the journey. The system includes an updater mechanism configured to refine the energy prediction algorithm for the vehicle by determining for each of a number of historical journeys undertaken by the vehicle, an error between an actual vehicle energy usage for the historical journey and a predicted energy usage derived using the energy prediction algorithm for the historical journey, each historical journey of the of the number of historical journeys being associated with a respective error of a set of errors. An aggregate error is calculated from the set of errors. The updater is arranged to adjust the energy prediction algorithm to reduce the aggregate error.

FIELD OF THE INVENTION

The invention relates to a system and method for determining the energyrequirement of a vehicle for a journey, the invention is particularlyapplicable, though does not exclusively relate, to electric vehicles.The system and method may be used as part of a vehicle schedulingsystem.

BACKGROUND TO THE INVENTION

Existing vehicle range prediction systems function by extrapolatingrecent consumption data of the vehicle, e.g. over the last 15 minutes,to provide an indication of the remaining range of the vehicle beforerefueling or recharging. They do not take into account the full natureof the journey to be undertaken by the vehicle and as a consequence theranges returned are often inaccurate. This is particularly problematicfor vehicles which have a long refueling/recharging interval or whererefueling/recharging stations are sparse.

The present invention was developed to ameliorate the above problems.

SUMMARY OF THE INVENTION

In a first aspect there is provided system for determining an energyrequirement of a vehicle for a journey, the system adapted to: obtainjourney data relating to the journey; obtain vehicle data associatedwith the vehicle; and obtain non-vehicle data; the system comprising apredictor mechanism to predict, using an energy prediction algorithm, avehicle energy requirement for the journey from the journey data,vehicle data and non-vehicle data; the system comprising an updatermechanism configured to adjust the energy prediction algorithm for thevehicle by: determining for each of a number of historical journeysundertaken by the vehicle, an error between an actual vehicle energyusage for the historical journey and a predicted energy usage derivedusing the energy prediction algorithm for the historical journey fromhistorical journey data, historical vehicle data and historicalnon-vehicle data, each historical journey of the of the number ofhistorical journeys being associated with a respective error of a set oferrors; determining an aggregate error from the set of errors; andadjusting the energy prediction algorithm to reduce the aggregate error.

In a second aspect there is provided a computer implemented method fordetermining energy requirement of a vehicle for a journey, the methodcomprising: obtaining journey data; obtaining vehicle data associatedwith the vehicle; obtaining non-vehicle data; predicting using apredictor mechanism with an energy prediction algorithm, a vehicleenergy requirement for the journey from the journey data, vehicle dataand non-vehicle data; adjusting the energy prediction algorithm for thevehicle by: determining for each of a number of historical journeysundertaken by the vehicle, an error between an actual vehicle energyusage for the historical journey and a predicted energy usage derivedusing the energy prediction algorithm for the historical journey fromhistorical journey data, historical vehicle data and historicalnon-vehicle data, each historical journey of the of the number ofhistorical journeys being associated with a respective error of a set oferrors; determining an aggregate error from the set of errors; andadjusting the energy prediction algorithm to reduce the aggregate error.The method may include updating the energy prediction algorithm with theadjusted energy prediction algorithm.

By adjusting the energy prediction algorithm to minimise the aggregateerror for historical journeys, the algorithm more accurately predictsthe energy requirement for future journeys undertaken by a specificvehicle and driver combination.

In this application the term ‘driver’ is taken to include an electronicsystem used to drive an autonomous vehicle as well as, moreconventionally, a human.

The following features apply to both aspects of the invention.

Where the vehicle's energy store is a chemical energy store, such as forexample, a hydrogen cell or gasoline tank, the energy on board data maycomprise an indication of the fuel level and/or pressure. Where theenergy store includes one or more batteries, capacitor or the like, theenergy on board data may include an indication of the state of charge.

The system may be arranged to retrieve vehicle data from the vehicle.Vehicle data may comprise variant vehicle data, that being data expectedto change over time, such as on an inter journey or intra-journey basis,and invariant vehicle data which is expected to remain substantiallystatic. Variant vehicle data may be retrieved repeatedly from thevehicle, e.g. at least every 60 seconds, whilst the vehicle is inoperation. This ensures the predictor mechanism can use relativelyaccurate data of the vehicle's condition to determine a prediction. Italso means that the location of the vehicle can be known to a reasonableaccuracy so that that the system can determine when the vehicle hasfinished a journey.

Examples of variant vehicle data include one or more of: location of thevehicle (e.g. provided through a global navigation system), state ofhealth of the energy storage system (e.g. degradation of maximum storagecapacity over time), vehicle system temperature(s) (e.g. the temperatureof the energy store), and tyre pressures. This data may be obtained fromthe vehicle's computer.

The system may include a vehicle device (e.g. a computing device)arranged to receive the vehicle data from the vehicle (e.g. via one ormore sensors) and transmit the received vehicle data, typicallywirelessly, for receipt by the predictor mechanism. In one arrangementthe vehicle device is connected to a computer of the vehicle, thevehicle's computer being connected to vehicle sensors. In a variantembodiment, the vehicle device comprises the vehicle's computer arrangedto directly transmit the vehicle data. Vehicle data may be received by aprediction system that includes the predictor mechanism and updatermechanism.

Invariant vehicle data may include one or more of: vehicle dimensions;kerb weight; drag coefficients of the vehicle's chassis; auxiliary loads(e.g. headlamp consumption, interior heating system consumption) andtype of tyres fitted. As this information is not expected to change on aper journey basis, it will not typically be obtained directly from thevehicle but rather from other sources such as vehicle specificationspublished by the manufacturer and user input. Invariant vehicle data maybe obtained when the vehicle is added to the system or in the firstinstance that a vehicle of the same make is added to the system.

The system may be arranged to determine the energy requirement formultiple vehicles in which case the system may obtain vehicle data fromeach vehicle for which journey predictions are made. The system may bearranged to hold a separate energy prediction algorithm for eachvehicle. The system may be arranged to update each energy predictionalgorithm based on the historical journeys carried out by the vehicle towhich it is associated. As such each energy prediction algorithm will,over time, be tuned to reflect the differences in actual energy usage byits associated vehicle as a consequence of different characteristics,aging and its driver's driving style.

Both variant and invariant vehicle data may be used to predict thevehicle's energy requirement for a journey.

Journey data may comprise geographical information (e.g. geographicalcoordinates) e.g. of start and end points of the journey, waypointand/or other routing information. It may also include furtherinformation such as one or more of: payload (e.g. number of passengers),route restrictions (e.g. avoid tolls) and speed restrictions imposed onthe vehicle (e.g. through the presence of an on-board speed restrictor).

The system may be arranged to receive a request for a prediction for anenergy requirement for a journey from a journey request device, forexample, a user via a computer, phone; a satellite navigation system ora vehicle scheduling system. The request may include the journey data.

Non-vehicle data may include one or more of: local weather (i.e. asapplicable to the journey) e.g. wind speed, precipitation, temperature,air pressure, visibility, humidity and cloud cover); topography of theroute; route distance; traffic information (e.g. average vehicle speedsat one or more sections along the route; stopping and turninginformation—e.g. number of stops and turns; road surface—type thereofand/or condition; and sunset/sunrise times. Non-vehicle data may beretrieved, at least in part, from one or more third party providers,e.g. metrological organisations and traffic information services.Non-vehicle data may be received, at least in part, via the internet.The system may use the journey data to obtain the relevant non-vehicledata for the journey.

The system may be adapted to retrieve energy on board data from thevehicle. The energy on board data may be used to derive the actualvehicle energy usage for the historical journey.

The actual vehicle energy usage may be derived using energy on boarddata retrieved from the vehicle at the beginning and end of thehistorical journey. The system may determine that a journey has endedusing geographical information retrieved from the vehicle and/or by theuser indicating the journey has ended.

The system may be adapted to use the energy on board data and aprediction of the vehicle energy required for a journey from thepredictor mechanism to provide an output indicating if the journey ispossible with the current energy on board the vehicle.

The system may be adapted to use the energy on board data and aprediction of the vehicle energy required for a journey from thepredictor mechanism to provide an indication of the remaining power onboard the vehicle that can be expected at the end of the journey

The system may be arranged to store in a memory store, as historicaldata, one or more of the journey data; vehicle data; non-vehicle data;or data derived from one more of journey data, vehicle data andnon-vehicle data; used and/or derived by the predictor mechanism todetermine a prediction for a journey. The stored historical data may beused by the updater mechanism to determine a prediction for that journeyas a historical journey. The memory store may also hold the energyprediction algorithm used by the predictor mechanism to predict theenergy requirement for the journey.

Predicting an energy requirement for a vehicle for a journey maycomprise determining multiple factors for the journey, each factorderived from one or more of the vehicle data, non-vehicle data andjourney data; applying a weighting for each factor; and combining theweighted factors to derive the overall energy requirement.

The factors may include one or more of: journey distance, height ofassent, height of decent, one or more speeds along the route of thejourney; expected frequency of stops/starts; payload; location ofre-fueling station; auxiliary loads; drag and impact of energy recoverysystem.

The journey distance factor may, for example, be derived from journeydata, e.g. start and end point locations, and non-vehicle data, e.g.mapping data. An auxiliary load factor, for example, may be derivedusing invariant vehicle data, e.g. wattage of headlamps and heater andnon-vehicle data, e.g. outside temperature, visibility andsunset/sunrise time.

The above factors relate to concrete attributes associated withcalculation of kinetic energy, potential energy, drag energy etc.Alternatively or in addition, abstract factors each derived from adifferent combination one or more of the vehicle data, journey data andnon-vehicle data but having no direct relation to real worldmeasurements of energy, may be used instead.

The system may learn the specific individual contributions of eachfactor to the overall energy consumption for the vehicle and driver fora journey to make better predictions.

In an implementation of this, the system may be arranged to update theenergy prediction algorithm by adjusting the weightings for the factorsto minimise the aggregate error.

For each historical journey the updater may be arranged to derive apredicted energy usage using the energy prediction algorithm by:retrieving and/or determining values for multiple factors for thehistorical journey, the factors being derived from one or more of:journey data associated with the historical journey, vehicle dataassociated with the historical journey and non-vehicle data associatedwith the historical journey; applying a weighting to each factor; andcombining the weighted factors;

and in which the updater is further arranged to apply an adjustment to aweighting for one of the factors to reduce the aggregate error, theadjustment being applied to the corresponding factor for each of thehistorical journeys.

The system may include a prediction system that includes the functionsof the predictor mechanism and updater mechanism. The predictionmechanism may be connected to a network to receive or more of thevehicle information, non-vehicle information and journey requests viathe network.

The functions of the predictor mechanism and updater mechanism may beimplemented by suitably programmed circuitry including one or moreprocessors and memory using techniques known to those skilled the art.

In another aspect, the method may be embedded in a computer programproduct embodied in one or more non-transitory computer readablemedium(s), which comprise all the features enabling the implementationof the methods,

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described by way of example with reference tothe drawings, as follows:

FIG. 1 is a schematic of a system for determining an energy requirementof a vehicle for a journey;

FIG. 2 is a schematic of the process for deriving an energy requirementprediction for a journey; and

FIG. 3 is a flow chart of the process for updating the algorithm used toderive the energy requirement prediction for the journey.

DETAILED DESCRIPTION

As required, detailed embodiments are disclosed herein; however, it isto be understood that the disclosed embodiments are merely examples andthat the systems and methods described below can be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present subject matter in virtually anyappropriately detailed structure and function. Further, the terms andphrases used herein are not intended to be limiting, but rather, toprovide an understandable description of the concepts.

The terms “a” or “an”, as used herein, are defined as one or more thanone. The term plurality, as used herein, is defined as two or more thantwo. The term another, as used herein, is defined as at least a secondor more. The terms “including” and “having,” as used herein, are definedas comprising (i.e., open language). The term “coupled,” as used herein,is defined as “connected”, although not necessarily directly, and notnecessarily mechanically. The term “configured to” describes hardware,software or a combination of hardware and software that is adapted to,set up, arranged, built, composed, constructed, designed or that has anycombination of these characteristics to carry out a given function. Theterm “adapted to” describes hardware, software or a combination ofhardware and software that is capable of, able to accommodate, to make,or that is suitable to carry out a given function.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription herein has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to theexamples in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope of the examples presented or claimed. The disclosedembodiments were chosen and described in order to explain the principlesof the embodiments and the practical application, and to enable othersof ordinary skill in the art to understand the various embodiments withvarious modifications as are suited to the particular use contemplated.It is intended that the appended claims below cover any and all suchapplications, modifications, and variations within the scope of theembodiments.

With reference to FIG. 1 there is shown a system 1 for determining anenergy requirement of a vehicle for a journey. The system 1 comprises avehicle device 2 that is located in a vehicle 3, and a prediction system4.

The prediction system 4 comprises a predictor mechanism 4A, memory store4B and updater mechanism 4C. A memory store may include any one or moreof the following: volatile memory, non-volatile memory, computer storagesystem, and the like. The prediction system 4 is implemented by one ormore suitably programmed processors communicatively coupled with memorystores and other computer components, using techniques known to thoseskilled in the art. A typical combination of hardware and software usedto implement the prediction system 4 could be a general purpose computersystem with a computer program that, when being loaded and executed,controls the computer system such that it carries out the methodsdescribed herein.

The vehicle device 2, which comprises one or more processorscommunicatively coupled with memory stores and other computercomponents, is arranged to receive energy on board data together withother current vehicle data including geolocation data from the vehicle3, and to relay the current vehicle data via a wireless transmitter 2A(which could be a wireless transceiver) via a network 5 that includes awireless receiver (not shown), to the prediction system 4.

In one arrangement the vehicle device 2 is arranged to be connected toan on-board computer of the vehicle and receive said vehicle data fromthe computer; however, in another arrangement the computer may itself bearranged to transmit the vehicle information and thus the vehicle devicemay be omitted. The functions of the device may be carried out by amobile (cellular) phone running a suitable application; where this isso, and the mobile phone is on-board the vehicle, geolocationinformation of the vehicle may be derived from the mobile phone's ownglobal navigational chip.

The system 1 is adapted to receive a request for a journey predictionfrom a journey request device 6, e.g. from a user (who may be in thevehicle) via a computer, satellite navigational system or a vehiclejourney scheduling system such as might be used by a taxi or couriercompany. The request identifies the vehicle and driver combination thatwill carry out the journey for which the prediction is requested andincludes journey information identifying the destination and anywaypoints. Favourably the journey information also includes furtherinformation such as payload (e.g. number of passengers), routerestrictions (e.g. avoid tolls) and speed restrictions imposed on thevehicle.

The journey prediction request is received by the prediction system 4via the network 5. In response the predictor 4A, implementing an energyusage algorithm stored in memory store 4B, derives an energy requirementprediction using the received current variant vehicle data from thevehicle device 2, together with invariant vehicle data pertaining to thevehicle from memory store 4B and non-vehicle data pertaining to thejourney from external source 7 (e.g. third party data provided) throughnetwork 5. A safety margin is applied to the calculated prediction toaccount for expected error in the predicted energy usage for the journeycompared with the actual energy usage for the journey. The derivedprediction (including safety margin) is returned via network 5 to thejourney request device 6.

Where the predicted energy requirement (including safety margin) exceedsthat of the energy on board the vehicle 3 as provided from vehicledevice 2 (e.g. where an electric car energy on board data may be derivedfrom state of charge data and battery health data), the predictor 4Areturns an indication to the journey request device 6 that the journeyis not achievable with the energy on board.

Where the request is submitted by a user it may be submitted via acomputer, including portable computer such as smart phone. The requestmay be received from a satellite navigational system and used by thesatellite navigation system to determine an appropriate route for a tripe.g. a route that is most suitable given the vehicle's state of charge.

Where the system is used in conjunction with a scheduling system 6, thescheduling system may use the system 1 to provide predictions for a setof vehicles available to carry out a journey that includes travelbetween at least two locations (e.g. as a request of a request for ataxi by a customer). The predictions may be used by a scheduling systemto determine which vehicle of the set of vehicles would be most suitableto carry out the journey given the vehicles' different locations andstates of charge.

The prediction together with the data used to create the prediction isstored in memory store 4B.

This process repeats each time a journey prediction request is made forthe vehicle driver combination.

Following completion of a journey, the system determines the actualenergy usage for the journey using the energy on board data from thevehicle device 2 at the start and end of the journey. The system 1 maydetermine that the vehicle has completed the journey though, e.g. anindicator from the user/scheduling system, and/or geolocationinformation received from the vehicle device 2.

With reference to FIG. 2, to derive an energy usage prediction for ajourney, the predictor 4A uses the vehicle data and non-vehicle data todetermine predicted values for individual energy component factorsF₁-F_(n) that summed together (or otherwise combined) provide thepredicted energy usage for the journey. A weighting W₁-W_(n) isassociated with each factor. The associated weighting is applied (e.g.multiplied) to each individual energy component factor F, e.g. F₁.W₁,F₂.W₂ . . . F_(n).W_(n) and the weighted factors summed:

${PE} = {\sum\limits_{1}^{n}{{Wn}.{Fn}}}$to provide a weighted predicted energy usage prediction (PE). For a newvehicle driver combination the weightings may be initially set so as tohave neutral affect, i.e. such that the weighted prediction is the sameas a non-weighted prediction. Alternatively, the weighting may beselected to reflect that of another similar vehicle for which historicaljourney information has already been recorded.

The system may determine the journey as including a route from adestination provided by the user/scheduling system, to a proximaterefueling station. The system may also determine the journey to includea route from the vehicle's current location to a starting location asprovided by the user/scheduling system.

Periodically or at some non-regular time interval, the prediction system4 runs the algorithm updater 4C to refine the algorithm.

With reference to FIG. 3, the updater 4C obtains information for a setof historical journeys held in memory store 4B (10), for each historicaljourney the updater retrieves (or derives) the values for the individualenergy component factors F (11), applies a weighting W to each factor Fand sums the weighted factors to derive a predicted energy usage for thehistorical journey (12). The predicted energy is compared with an actualenergy used for the journey as held in memory store 4B to determine anerror E for the journey (13). This leads to a set of errors E₁-E_(j)where j is the number of journeys in the historical set. The errorsE₁-E_(j) are summed to provide an aggregate error E_(t) (14).

The updater then adjusts a weighting W for a factor F (15), the sameadjustment is made to the same factor of each of the historical journeysleading to a new set of errors E₁-E_(j). If the adjustment results in areduced aggregate error E_(t), the algorithm stored in memory store 4Bis updated with the new weighting W (16). This process is iterated forthe weighting for each factor, e.g. W₁, then W₂ . . . to W_(n)(17).

This process is repeated until either the aggregate error E_(t) iswithin a desired tolerance or to a desired number of iterations (18).The refined algorithm is stored in memory store 4B. It will beappreciated that the order that the factor weighting are altered doesnot have to be specific.

By varying the weighting, the algorithm is refined to account foridiosyncrasies of the particular vehicle and driver combination thataffect energy consumption, e.g. the driver's driving style and changesin the vehicle's condition and as such becomes more accurate atpredicting the energy requirement for future journeys.

It will be appreciated to those skilled in the art that in addition tothe gradient descent approach described above, there are numerous otherpossible methods of optimising the algorithm to lower aggregate errorE_(t), including for example, solving a set of simultaneous equations.It will be appreciated that the exact method used is not necessarily ofimport.

As part of the refinement process, the updater 4C determines the safetymargin that is applied to the calculated prediction PE. The size ofsafety margin is related to the sizes of one or more of the individualerrors E₁-E_(j), for the historical journeys derived from the refinedalgorithm. As the individual sizes of the errors E₁-E_(j) reduces, thesize of the safely margin used will also reduce. The size of the safetymargin may be derived using standard deviation of the individual errors,by size of the largest individual error or by some other means.

In a variant embodiment, the system as a whole may be located in thevehicle making the journey and may, for example, form a part of thevehicle's satellite navigation system with the functions of theprediction system carried out by the vehicle's computer. In such anarrangement the vehicle device may be omitted. Requests for journeyinformation may be provided by a user via an interface of the vehicle'ssatellite navigation system.

The system may be adapted to determine an energy requirement for ajourney without live vehicle data. In this variant arrangement, thesystem may use the most recent vehicle data it has for the vehicleand/or values provided by a user or scheduling system.

The prediction system 4, vehicle device 2 and journey request device 6discussed above with reference to FIG. 1 can each include variouscomponents. Some of these components that are usable by certain exampleimplementations of the computer system will be discussed below.

The prediction system 4 may comprise at least one processor/controllercommunicatively coupled with persistent (non-volatile) memory 4B of theprediction system 4. A bus architecture communicatively couples theprocessor/controller of the prediction system 4 with the memory 4B andother components of the prediction system 4. The bus architecturefacilitates communication between the various system components in theprediction system 4.

The processor/controller of the prediction system 4 is communicativelycoupled with one or more network interface devices of the predictionsystem 4. For example, and not for limitation, a network interfacedevice can include at least one wireless communication transceiverdevice (e.g., at least one wireless communication access pointtransceiver device). The one or more network interface devices of theprediction system 4 are communicatively coupled with one or morecommunication networks 5 in order to communicate with one or more of thejourney request device, vehicle device and external source e.g. forthird party data. The network interface device can communicate with oneor more communication networks such as a local area network (LAN), ageneral wide area network (WAN), and/or a public network (e.g., theInternet).

The journey request device 6 may comprise at least oneprocessor/controller of its own of its own communicatively coupled witha memory, which can include main memory and persistent (non-volatile)memory. A bus architecture communicatively couples theprocessor/controller with the memory and other components of the journeyrequest device 6. The bus architecture facilitates communication betweenthe various system components in the journey request device 6.

The processor/controller of the journey request device 6 iscommunicatively coupled with at least one user interface of theelectronic device. The user interface comprises a user input interfaceand a user output interface. Examples of elements of the user inputinterface can include a keyboard a keypad, a mouse, a track pad, atouchpad, a touch screen, and a microphone that receives audio signals.The received audio signals, for example, can be converted to electronicdigital representations of the audio signals and stored in memory, andoptionally can be used with voice recognition software executed by theprocessor/controller to receive user input data and commands. Examplesof elements of the user output interface can include a display, aspeaker, one or more indicator lights, one or more transducers thatgenerate audible indicators, and a haptic signal generator. Someexamples of displays are a liquid crystal display (LCD), a plasmadisplay, an organic light emitting diode (OLED) display, and others. Atouch screen display (also referred to as a touch input screen)functions both as an output interface device and as an input interfacedevice that can receive direct user input taking the form of “touches,”“swipes,” or “taps.”

The processor/controller of the journey request device 6 iscommunicatively coupled with one or more network interface devices. Forexample, and not for limitation, a network interface device can includeat least one wireless communication transceiver device (e.g., at leastone wireless communication access point transceiver device). The one ormore network interface devices are communicatively coupled with one ormore communication networks 5. The network interface device cancommunicate with one or more communication networks 5 such as a localarea network (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet).

The processor/controller of the journey request device may in onearrangement be communicatively coupled with a GPS receiver. That is, GPSdata received by the GPS receiver can be used by theprocessor/controller of the journey request device 6 to determine ageolocation for the journey request device 6. The geolocation of thejourney request device 6 may be taken as the geolocation of the vehicle.

The vehicle device 2 may comprise at least one processor/controllercommunicatively coupled with a memory of the vehicle device 2, which caninclude main memory and persistent (non-volatile) memory. A busarchitecture of the vehicle device 2 communicatively couples the vehicledevice's 2 processor/controller with the vehicle device's 2 memory andother components of the vehicle device 2. The bus architecturefacilitates communication between the various system components in thevehicle device 2.

The processor/controller of the vehicle device 2 is communicativelycoupled with one or more network interface devices of the vehicle device2. For example, and not for limitation, a network interface device caninclude at least one wireless communication transceiver device (e.g., atleast one wireless communication access point transceiver device). Theone or more network interface devices of vehicle device 2 arecommunicatively coupled with one or more communication networks 5. Thenetwork interface device of vehicle device can communicate with one ormore communication networks 5 such as a local area network (LAN), ageneral wide area network (WAN), and/or a public network (e.g., theInternet).

The processor/controller of vehicle device 2 may be communicativelycoupled with a GPS receiver of the vehicle device 2. That is, GPS datareceived by the GPS receiver of the vehicle device 2 can be used by theprocessor/controller of the vehicle device 2 to determine a geolocationfor the vehicle device 2 and thus the vehicle 3.

The processor/controller of the vehicle device 2 may be communicativelycoupled with one or more sensor devices (or sensors) of the vehicle 3either directly or via a computer of the vehicle. Sensors can includevarious types of sensor devices that provide sensor data to theprocessor/controller of the vehicle device 2. Examples include one ormore of tire pressures, energy of board sensors, e.g. a fuel levelsensor or state of charge sensor, vehicle system temperature sensors,sensors to detect the operational state of auxiliary electrical devices,e.g. internal heater of vehicle and headlamps.

One or more of the predictor system, vehicle device and journey requestdevice may include power supply system that provides power for itsoperation.

The processor/controller of the journey request device 6, andprocessor/controller of the vehicle device 2 may communicate with theprocessor/controller of the prediction system 4 via the communicationnetwork 5.

In an alternative arrangement the functions of the predictor mechanism,updater, vehicle device and journey request device may be implemented bya single computer system located in the vehicle. In this arrangement thecomputer system may have a processor/controller coupled with persistent(non-volatile) memory, a bus architecture communicatively coupling theprocessor/controller with the memory and other components of thecomputer system, such as sensors devices, GPS receiver and userinterface devices.

As will be appreciated by one of ordinary skill in the art, a system, amethod, or a computer program product, may implement various embodimentsof the claimed invention. Accordingly, aspects of the claimed inventionmay take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects.Furthermore, aspects of the claimed invention may take the form of acomputer program product embodied in one or more non-transitory computerreadable medium(s) having computer readable program code embodiedthereon.

Although specific embodiments of the subject matter have been disclosed,those having ordinary skill in the art will understand that changes canbe made to the specific embodiments without departing from the scope ofthe disclosed subject matter. The scope of the disclosure is not to berestricted, therefore, to the specific embodiments, and it is intendedthat the appended claims cover any and all such applications,modifications, and embodiments within the scope of the presentdisclosure.

The invention claimed is:
 1. A system for determining a prediction of anenergy requirement of a vehicle for an expected vehicle journey, thesystem carried by the vehicle, and the system comprising: one or morevehicle sensors carried by the vehicle, the vehicle sensors coupled withthe vehicle and sensing physical states and conditions of the vehicle ora component of the vehicle, and the vehicle sensors providing sensordata in response to the sensing by the vehicle sensors; one or moreprocessors communicatively coupled with the one or more vehicle sensors;one or more memory stores, and one or more network interface deviceswhich are communicatively couple with one or more communicationnetworks, the one or more processors being configured to: receive arequest, from a journey request device, for a prediction of an energyrequirement of a vehicle for an expected vehicle journey, the requestincluding journey data relating to the expected vehicle journey; obtainvehicle data associated with the vehicle including the sensor data fromthe one or more vehicle sensors; and obtain, via the one or more networkinterface devices, non-vehicle data at least in part provided throughthe one or more communication networks and from one or more remoteexternal information sources; the system comprising an interface toobtain at least the journey data relating to the expected vehiclejourney; the system comprising a predictor mechanism configured topredict, using an energy prediction algorithm, a vehicle energyrequirement for the expected vehicle journey from the journey data, thevehicle data, and the non-vehicle data; the system comprising an updatermechanism configured to adjust the energy prediction algorithm for thevehicle by: determining for each of a number of historical journeysundertaken by the vehicle, an error between an actual vehicle energyusage for the historical journey and a predicted energy usage derivedusing the energy prediction algorithm for the historical journey fromhistorical journey data, historical vehicle data and historicalnon-vehicle data, each historical journey of the number of historicaljourneys being associated with a respective error of a set of errors;determining an aggregate error from the set of errors; and adjusting theenergy prediction algorithm by an adjustment based on determination of areduction of the aggregate error by applying the adjustment to theenergy prediction algorithm used to derive the predicted energy usagefor each of the historical journeys associated with the respective errorof the set of errors; and wherein for each historical journey theupdater is arranged to derive the predicted energy usage using theenergy prediction algorithm by: retrieving and/or determining values formultiple factors for the historical journey, the factors being derivedfrom one or more of: the historical journey data associated with thehistorical journey, vehicle data associated with the historical journey,and non-vehicle data associated with the historical journey; applying aweighting to each factor; applying the weighted factors in the energyprediction algorithm to derive the predicted vehicle energy requirementfor the historical journey; in which the updater is further arranged toapply the adjustment to a weighting for one of the factors based on thedetermination of the reduction of the aggregate error by applying theadjustment, the adjustment being applied to the corresponding factor foreach of the historical journeys; and the predictor algorithm predicting,using the adjusted energy prediction algorithm, the predictioncomprising the vehicle energy requirement for the expected vehiclejourney, and the predictor mechanism returning the prediction, based onthe predicted vehicle energy requirement, to a network interface deviceof the journey request device, wherein the journey request devicecomprises a vehicle scheduling system, wherein the vehicle schedulingsystem determines whether the vehicle, selected from a set of vehicles,would be most suitable to carry out the expected vehicle journey, basedon the prediction.
 2. A system according to claim 1, wherein the systemis arranged to store in a memory store, in the one or more memorystores, one or more of the: journey data; vehicle data; non-vehicledata; data derived from one or more of the journey data, vehicle dataand non-vehicle data; and the weighted factors used by the predictormechanism to predict the vehicle energy requirement for the expectedvehicle journey.
 3. A system according to claim 1 adapted to retrieveenergy on board data from the vehicle.
 4. A system according to claim 3wherein the system is adapted to use the energy on board data todetermine the actual vehicle energy usage for the historical journeyundertaken by the vehicle.
 5. A system according to claim 3 wherein thesystem is adapted to use the energy on board data and the prediction ofthe vehicle energy required for the expected vehicle journey from thepredictor mechanism to provide the output signal indicative of whetherthe expected vehicle journey is made by the vehicle with current energyon board the vehicle.
 6. A system according to claim 1, wherein thesystem is arranged to receive energy on board data and the vehicle datafrom the vehicle and wirelessly transmit, via the one or morecommunication networks, the received energy on board data and thevehicle data to the predictor mechanism located remote to the vehicle.7. A system according to claim 1, wherein the predictor mechanism isadapted to include a safety margin within the predicted vehicle energyrequirement for the expected vehicle journey, and in which the updatermechanism acts to adjust a size of the safety margin according to sizesof individual errors of the historical journeys.
 8. A system fordetermining a prediction of an energy requirement of a vehicle for anexpected vehicle journey, the system carried by the vehicle, and thesystem comprising: one or more vehicle sensors carried by the vehicle,the vehicle sensors coupled with the vehicle and sensing physical statesand conditions of the vehicle or a component of the vehicle, and thevehicle sensors providing sensor data in response to the sensing by thevehicle sensors; one or more processors communicatively coupled with theone or more vehicle sensors; one or more memory stores, and one or morenetwork interface devices which are communicatively couple with one ormore communication networks, the one or more processors being configuredto: receive a request, from a journey request device, for a predictionof an energy requirement of a vehicle for an expected vehicle journey,the request including journey data relating to the expected vehiclejourney; obtain vehicle data associated with the vehicle including thesensor data from the one or more vehicle sensors; and obtain, via theone or more network interface devices, non-vehicle data at least in partprovided through the one or more communication networks and from one ormore remote external information sources; the system comprising aninterface to obtain at least the journey data relating to the expectedvehicle journey; the system comprising a predictor mechanism configuredto predict, using an energy prediction algorithm, a vehicle energyrequirement for the expected vehicle journey from the journey data, thevehicle data, and the non-vehicle data; the system comprising an updatermechanism configured to adjust the energy prediction algorithm for thevehicle by: determining for each of a number of historical journeysundertaken by the vehicle, an error between an actual vehicle energyusage for the historical journey and a predicted energy usage derivedusing the energy prediction algorithm for the historical journey fromhistorical journey data, historical vehicle data and historicalnon-vehicle data, each historical journey of the number of historicaljourneys being associated with a respective error of a set of errors;determining an aggregate error from the set of errors; and adjusting theenergy prediction algorithm by an adjustment based on determination of areduction of the aggregate error by applying the adjustment to theenergy prediction algorithm used to derive the predicted energy usagefor each of the historical journeys associated with the respective errorof the set of errors; and wherein for each historical journey theupdater is arranged to derive the predicted energy usage using theenergy prediction algorithm by: retrieving and/or determining values formultiple factors for the historical journey, the factors being derivedfrom one or more of: the historical journey data associated with thehistorical journey, vehicle data associated with the historical journey,and non-vehicle data associated with the historical journey; applying aweighting to each factor; applying the weighted factors in the energyprediction algorithm to derive the predicted vehicle energy requirementfor the historical journey; in which the updater is further arranged toapply the adjustment to a weighting for one of the factors based on thedetermination of a reduction of the aggregate error by applying theadjustment, the adjustment being applied to the corresponding factor foreach of the historical journeys; and the predictor algorithm predicting,using the adjusted energy prediction algorithm, the predictioncomprising the vehicle energy requirement for the expected vehiclejourney, and the predictor mechanism returning the prediction, based onthe predicted vehicle energy requirement, to a network interface deviceof the journey request device, wherein the journey request devicecomprises at least one of: a driver via a computer, phone, or asatellite navigation system, in the vehicle, wherein the prediction isprovided in an output signal via an interface in the vehicle,communicatively coupled with the journey request device; or a singlecomputer system located in the vehicle, wherein the prediction isprovided in an output signal via at least one of a user interface or anetwork interface device, of the journey request device; and wherein thejourney request device, based on the prediction and the expected vehiclejourney, determines at least one of an appropriate route for a trip forthe vehicle according to the expected vehicle journey or that therequested expected vehicle journey is not achievable based on the energyon board the vehicle and/or a condition associated with the expectedvehicle journey.